

QbD implementation in Generic Industry:
Overview and Case-Study
Inna Ben-Anat,
Ben Anat QbD Strategy Leader,
Leader Teva Pharmaceuticals
IFPAC
JAN
2013
R&D
Three Core Components of QbD and Generic Industry:

How Do They Overlap

Quality by Design
Clearly
Cl
l d
defining
fi i the
h iintended
d d purpose off
the future developed product and design
this product to fit its purpose
2.
Understanding what attributes of this
product are critical so it (product) will keep
serving
g its intended p
purpose
p
1
1.
The connectio
T
on is clear
1
1.
Generic Industry
2.
3.
Enhanced understanding ‘what’ impacting
the critical quality attributes and ‘how’
(materials, process, packaging etc) ; define
control strategies so that the intended
3.
purpose of the product will reproducibly
maintain
i t i itits iintegrity
t it
Reproducibly
R
d ibl M
Making
ki “A d
drug product
d t
that is comparable to brand/reference
listed drug product in dosage form,
strength route of administration
strength,
administration, quality
and performance characteristics, and
intended use"
Providing uninterrupted supply of high
quality and affordable medication to our
patients
Efficiency and Speed


QbD for Generics: Finding the right balance between
Speed, Efficiency and Excellence

Overview of QbD (GPhA, May 2012)


QbD Guide for Generics:
Step 1-Product Design

RLD Characterization

Quality Target Product Profile

Critical Quality Attributes
GPhA/FDA CMC Workshop, May 2012


QbD Guide for Generics:
Step 2 - What are the potential Risks
Risk Assessment Defines the Development Strategy
What are the Risks?...
 API
 Excipients
 Formulation and Process
 Equipment
i
 Testing
 Packaging
 …
How do we stay efficient
 Effective Prior Knowledge utilization
and management
 Generic Industry has a lot of
information and in-house
knowledge available

Data bases of pre
pre-created
created
Ishikawa diagrams in order to
harmonize and streamline the Risk
Assessment process
p

Historical data-mining

Historical Data Mining: Drug Layering of Pellets Example

Example: Previously developed product,
multiply batches are available for Data Mining:
In-Process Pellets Assay vs. Fines Correlation
Based on the found relationship
relationship, Assay
decreases ~0.6% with each % fines
How do we control low % fines
by process parameters
(Drug Layering)…
Layering)
‘All examples are for illustration purposes only’

Historical Data Mining: Drug Layering of Pellets Example
Actual Processing Parameters from all available historical lots were
collected and ‘data‘data-mined’
Partition per most critical factor affecting % Fines
All Rows
Count
31 LogWorth Difference
Mean
3.666129
3
666129 1.6232558
1 6232558
1 98596
1.98596
Std Dev 2.4278793
Slit Temp Actual (°C) -max<74.3
Count
19 LogWorth Difference
Mean
2.8973684 0.6352248
1.21364
Std Dev 2.1367027
Exhaust Temp-AVG<44.4
Count
11
Mean
2.3863636
Std Dev 2.1165362
Slit Temp Actual (°C) -max>=74.3
Count
12
Mean
4.8833333
Std Dev 2.4430061
1. Most Significant parameters affecting
%Fines are Slit Temp and Exhaust
Temp
2. Lower Slit Temperature (<74˚C)and
lower Exhaust Temperatures (<44
(<44˚C)
C)
will generate less % Fines
Exhaust Temp-AVG>=44.4
Count
8
Mean
3.6
Std Dev 2.0894223
Potential DOE Factors for future similar
products/processes or for further process
fine-tuning
‘All examples are for illustration purposes only’
QbD Guide for Generics:
Step 3 - Plan the right/relevant Experiments


Efficient and Informative DOE: CQAs= f (CPPs, CMAs)

How do we stay efficient
o
Effective Prior Knowledge Utilization
 What do we vary and what do we fix?
 What target and range do we evaluate and why?
 What statistical model do we use and why? (Can we assess
what interactions are most likely to occur? Can we assess what factors
would have non linear relationship with the response?)
o
Modern DOE techniques for efficient yet powerful designs (DOptimum, I-Optimum)
o
Monte Carlo Simulations to assess the process robustness using
historical data to assess expected variabilityy

Let’s take a typical manufacturing process for tablets
 as an example to start with…
Wet Granulation
Fluid Bed Drying
Milling
Blending
Compression
How many potentially Critical Process Parameters do
we need to assess?
5? 10? 25?


High Shear Wet Granulation:
> 40 potential CPPs…
High Shear Wet Granulation
Fish-Bone Diagram
CQAs
>40…
40

Fluid Bed Drying:
> 30 potential CPPs…

Fluid Bed Drying
Fish-Bone Diagram
CQAs

 A Typical Manufacturing Process for Tablets…
HS Wet Granulation
Fluid Bed Drying
Milling
Blending
Compression
For a process involving the above unit operations we may end up with
over 100 potential CPPs.
g it?
How do we manage

 Effective Knowledge Management !
Prior Knowledge Utilization
Blending Unit Operation
CQAs
4 critical variables are left for assessment, the rest are kept at
constant and monitored
Design Variable
Prior Experience/Fixed
Justify!!

 Effective Knowledge Management !
With efficient Prior Knowledge utilization, we can end up with
8-16 trials for Experimental Design- feasible!
JMP® Statistical Software from SAS
Main effects
Interactions
Prior
Knowledge

Efficient and Informative Design of Experiments
•
Brainstorming sessions will identify the design factors and their
ranges, while previous knowledge should be effectively utilized to
identify those and limit them to the most critical ones
•
While conducting DoE, all parameters that are not studied should
be kept constant at their optimum fixed level (justify!) in order to
eliminate the noise and additional variation and increase the
effectiveness of the study
•
Prior to DoE execution, measurement’s system integrity and
sensitivity must be verified
•
There is a lot to learn from every DoE: if a factor was found to have
no effect, it can be used to minimize cost or increase
robustness by having it set on convenient level

DOE and Modeling:
Process Robustness and Monte Carlo Simulation

Monte Carlo Simulation: Predicted OOS Rate: ~0.02%
Distribution of the
predicted output
Predicted OOS rate
Estimated Process Variability
Estimated Analytical Variability
‘All examples are for illustration purposes only’


QbD Guide for Generics:
Step 4 - Define Control Strategies
Questions to ask ourselves:
1.
Did we evaluate the impact of CMAs and CPPs on CQAs? Did we find
any interactions? What do they mean for us?
2.
Do we have a robust and reproducible process? Do we know the impact
of raw materials variability? Did we identify potential sources of
variation?
3.
Did we establish meaningful In Process and Release specifications?
4.
Did we address scale-up
p challenges?
g
5.
…………………………………………..


Case-Study
IR Tablet
Tablet, Dry Granulation Process

Product
Development Outline
 Analysis of the reference listed drug (RLD)
 Defining
f
Quality
Q
Target Product Profile
f (QTPP)
(Q
)
 Identification of Critical Quality Attributes (CQAs)
potential risks related to Drug
g
 Identification and evaluation of p
Product Components (DS and Excipients – stability and
compatibility), Formulation and Manufacturing Process, etc.
 Screening and optimization of formulation
 Development of a robust process (DOE for high risk
parameters)
 Manufacture of the exhibit batch
 Establishment of control strategies
 QTPP

Component
Target
Justification
Dosage Form
Tablet
Administration Route
Oral
Dosage Design
Immediate release tablet
Strength
X and Y mgs
Bioequivalence
AUC and Cmax match RLD under food
Bioequivalent to RLD
Appearance
Both: Brown to orange elegant film coated tablet.
Dimensions similar to RLD
RLD.
X mg: round; Y mg: oval
Marketing
g requirement;
q
Needed for patient acceptability
Identity
Positive for API
Needed for labeled claim & therapeutic
efficacy
Assay
100% of label claim
Needed for therapeutic efficacy
Impurities
Specified and unspecified impurities meet ICH Q3B.
Needed to ensure safety
Disintegration
Comparable disintegration time as RLD in
appropriate media at room temperature
Pharmaceutical equivalence to RLD (possible
route of administration as suspension)
Content Uniformity
AV <15.0 (tested by weight variation)
Targeted for consistent clinical effectiveness
Residual solvents
Complies with USP <467>
Regulatory requirement. Needed to ensure
safety
Dissolution
USP Apparatus
pp
II, 50 rpm,
p 1000 mL 0.1M HCl,
370C. NLT 85Q is dissolved in 45min
Regulatory
g
y requirement
q
Stability
NLT 24 month shelf life
Needed for commercialization
Container closure
system
HDPE bottles with Child Resistant (CR) Caps and
appropriate desiccants , if required
Needed for safety and commercial
requirements
Pharmaceutical equivalence to RLD

 CQAs
CQA
Justification
Potentially affected by
Assay
Needed for therapeutic efficacy
Process
Impurity
Needed to ensure safety
Formulation & Process
Content
Uniformity
Needed for therapeutic efficacy of
each unit
Formulation & Process
Dissolution
Presumptive qualification for in vivo
release and therapeutic efficacy
Formulation & Process
Disintegration
Needed to ensure patient
compliance (suspension)
Formulation & Process

Formulation: Initial Risk Assessment and
studies conducted

Filler
DP CQA
Assay
type
& amount
Low
Formulation Attribute
Disintegrant Lubricant
Glid t
Glidant
type
type
amount
& amount
& amount
Low
Low
Low
Coating
C
ti
formulation
Low
Impurities
Low
Low
Low
Low
Low
Content
Uniformity
y
Low
Medium
Low
Low
Low
Dissolution
Medium
Low
High
Medium
Low
Disintegration
Medium
Low
High
Low
Low
Vary type & amount (control
strategy: optimized and
fixed))
Fix on high level based on
prior knowledge
Vary type & amount (control
t t
optimized
ti i d and
d fixed)
fi d)
strategy:

 Process Scheme
Pharmacy
Compression II
(cores)
Mixing I
Mixing IV & V
C
Cosmetic
ti C
Coating
ti
Milling I
(De-lumping)
Milling II
Mixing II+III
Compression I
(slugs)

 Initial Risk Assessment: Process
Unit Operations
DP CQA
Mixing I
Milling I
Mixing II+III
Medium
(De-lumping)
Medium
Compression I
(Slugs)
Low
Low
Impurities
Low
Medium
Low
Medium
Content
Uniformity
Low
Medium
Medium
Medium
Dissolution
L
Low
M di
Medium
L
Low
Hi h
High
Disintegration
Low
Low
Low
High
y
Assay
Unit Operations-cont'd
Operations cont'd
DP CQA
Mixing IV+V
Compression II
Coating
Low
Low
(Tablets)
Low
Low
Impurities
Medium
Low
Low
Medium
Content
Uniformity
Low
Low
Low
Low
Dissolution
High
Low
High
Medium
Disintegration
High
Low
High
Medium
y
Assay
Milling II

 Process Optimization DOE
 Based on prior knowledge, previous experience and initial feasibility
studies, the most potentially critical process parameters were chosen
for further evaluation in DOE study. Additional parameters were set at
their
h i optimum
i
fifixed
d constant llevell iin order
d to reduce
d
uncontrolled
ll d noise
i
and variability
(13 runs including 2 centers, D-Optimum Design using JMP software from SAS)
Unit Operation
Compression I
(Slugs)
Milling II
Compression II
(Tablets)
DOE Factors
Levels Used
-1
0
+1
Compression
force
Low
Medium
High
Compression
speed
Low
Medium
High
Mill type
Quadro
Quad
o
NA
Frewitt
e tt
Mill screen
0.6
NA
0.8
Compression
force
Low
Medium
High
Compression
speed
Low
Medium
High
Responses
1.
2.
Slug weight /RSD
Slug hardness
1.
2.
3.
PSD
S
Bulk & tap density
Hausner ratio/Flow
1.
2.
3.
4.
5.
Assay & impurities
Dissolution
Content Uniformity
Disintegration time
Tablet Hardness

Prediction Profilers:
Factors/Responses relationship-% on PAN (Fines)
Interaction: Mill screen impact
is low for Frewitt Type Mill

Prediction Profilers:
Factors/Responses relationship (Dissolution)

DOE Model Prediction vs. actual Exhibit Batch data
Hausner Ratio
1.31
Model
Predicted
Value
1.28
% Fines
19%
17%
Dissolution-T1
Dissolution
T1 AVG
(N=6)
Dissolution-T1 RSD
(N=6)
Dissolution-T3 AVG
(N=6)
UoC RSD
(N=10)
36%
36%
10.1%
8.8 %
69%
68 %
1.69 %
1.45 %
Selected Response
Exhibit Batch
Value
Good Correlation between Values p
predicted by
y DOE Model &
Actual Responses

 Process-Risk Mitigation, 1/2
Unit Operations
DP CQA
Mixing I
Assay
Controlled by
mixing
time/speed
Milling I
Controlled
by
S
Screen
size
Low
Low (Was
found not
critical)
Low
Controlled
by
Screen
size
Dissolution
Low
Low (Was
found not
critical)
Disintegration
Low
Impurities
p
CU
Low
Mixing II+III
Low
Compression
I (Slugs)
Low
Low
Low (Was
found not
critical)
Controlled by
mixing
time/speed
Low (Was
found not
critical)
Low
Low
Controlled by
slug
hardness

 Process-Risk Mitigation, 2/2
Unit Operations-cont'd
DP CQA
y
Assay
Milling II
Mixing IV+V
Compression II
(Tablets)
Coating
Low
Low
Low
Low
Low (Was
found not
critical)
Impurities
Low (Was
found not
critical)
Low
Low
Content
Uniformity
Low
Low
Low
Low
Controlled
by mill
type/ mill
screen
Low
Controlled by
core hardness
and
compression
speed
Controlled
by fixed
coating level
Dissolution
Disintegration
g
Low


Summary
 Despite all of the challenges, the Generics Industry acknowledges
p
gQ
QbD is the way
y forward,, g
gaining
g
that implementing
o Enhanced product and process understanding- robust
products and processes
o Id
Identification
tifi ti and
d control
t l off sources off variationi ti
f t and
faster
d
efficient tech transfers, greater process capability
 Efficient utilization of prior knowledge is a key to successful QbD
implementation in generics
 Real change will come if and when
o The risk/cost benefits are realized
o Playing field is leveled
o FDA review of the applications shows the benefits of QbD
32


-“What
“Wh should
h ld I do
d next?”
?”
-“Create
Create an action plan, Adopt the Big Q Concept
Concept”
Juran on Quality by Design